ContextObserved
Captured ad creative, associated prompts, advertiser presence, niche assignment, and other directly recorded market evidence.
Evidence-led research workflow
Research the ChatGPT Ads market from the agent you already use.
Investigate a niche, find comparable advertisers, inspect competitor patterns, and carry the evidence into a context-hint decision—all from Codex or Claude.
Captured creatives9,046
Advertisers analyzed2,737
Niches covered72
A useful agent does not merely produce a plausible strategy. It shows which market evidence supports the direction, which parts are inferred, and what remains unknown.
Path one
Map a niche before choosing the message.
Use niche research when the market category is known but the strongest conversation and intent lane is not. The workflow surfaces advertiser density, observed intent mix, representative inferred hints, and the available evidence boundary.
Example request
“Map the ChatGPT Ads market for accounting automation. Show the dominant buyer intents, representative advertisers, and the clearest underused targeting angle.”
The agent can combine list_niches with get_niche_intel, then explain where the evidence is strong enough to support a hypothesis.
Market → intent → angleResearch sequence
Best when you need category orientation before generating a context hint.
Path two
Build a defensible competitive set.
Start from the product brief rather than a guessed list of rivals. Similar-advertiser discovery identifies semantically close companies; advertiser intelligence then reconstructs their observed niche presence and inferred targeting patterns.
Example request
“Find advertisers closest to this product, inspect the top three, and separate observed facts from inferred audience, intent, and context-hint patterns.”
The workflow uses find_similar_advertisers and get_advertiser_intel. It does not claim access to private settings, spend, bids, or conversion data.
Brief → peers → patternsResearch sequence
Best when positioning or competitor assumptions need evidence before campaign planning.
Evidence model
Know what each statement can prove.
Research is useful only when the provenance travels with the answer. ContextHint separates captured observations, evidence-backed reconstruction, and advertiser-only performance facts.
Inferred
Reconstructed audience, intent, topic, competitor, and context-hint patterns supported by observed evidence and disclosed confidence.
Advertiser-only
Private settings, bids, spend, delivery diagnostics, conversions, and business outcomes remain outside the dataset.
Research FAQ
Use the output as evidence, not omniscience.
The strongest workflow makes uncertainty reviewable. It does not turn public observations into claims about another advertiser’s private account.
What can a ChatGPT Ads research agent investigate?
It can map an available niche, examine its advertiser and intent landscape, find advertisers similar to a product brief, and analyze one advertiser's observed niche presence and inferred targeting patterns.
Does the research reveal competitors' private context hints?
No. Competitor context hints are evidence-backed inferences reconstructed from captured ads and associated prompts, not literal settings retrieved from private advertiser accounts.
Can I use the research without generating a context hint?
Yes. Niche intelligence, advertiser intelligence, and similar-advertiser discovery are independent research workflows. Generate a hint only when the evidence supports moving from exploration to a targeting hypothesis.
How current is the evidence?
Results depend on the captured evidence available when the request is made. Responses should expose relevant methodology, confidence, freshness, and evidence links rather than presenting an inference as a private fact.